Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations73100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.2 MiB
Average record size in memory132.0 B

Variable types

DateTime1
Categorical9
Numeric8

Alerts

Competitor Pricing is highly overall correlated with PriceHigh correlation
Demand Forecast is highly overall correlated with Inventory Level and 1 other fieldsHigh correlation
Inventory Level is highly overall correlated with Demand Forecast and 1 other fieldsHigh correlation
Price is highly overall correlated with Competitor PricingHigh correlation
Units Sold is highly overall correlated with Demand Forecast and 1 other fieldsHigh correlation
Store ID is uniformly distributed Uniform
Product ID is uniformly distributed Uniform

Reproduction

Analysis started2025-03-21 13:34:17.518166
Analysis finished2025-03-21 13:34:22.953617
Duration5.44 seconds
Software versionydata-profiling vv4.15.1
Download configurationconfig.json

Variables

Date
Date

Distinct731
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size571.2 KiB
Minimum2022-01-01 00:00:00
Maximum2024-01-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-21T13:34:22.991620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:23.062113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Store ID
Categorical

Uniform 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size571.2 KiB
S001
14620 
S002
14620 
S003
14620 
S004
14620 
S005
14620 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters292400
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS001
2nd rowS001
3rd rowS001
4th rowS001
5th rowS001

Common Values

ValueCountFrequency (%)
S001 14620
20.0%
S002 14620
20.0%
S003 14620
20.0%
S004 14620
20.0%
S005 14620
20.0%

Length

2025-03-21T13:34:23.124933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-21T13:34:23.168974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
s001 14620
20.0%
s002 14620
20.0%
s003 14620
20.0%
s004 14620
20.0%
s005 14620
20.0%

Most occurring characters

ValueCountFrequency (%)
0 146200
50.0%
S 73100
25.0%
1 14620
 
5.0%
2 14620
 
5.0%
3 14620
 
5.0%
4 14620
 
5.0%
5 14620
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 292400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 146200
50.0%
S 73100
25.0%
1 14620
 
5.0%
2 14620
 
5.0%
3 14620
 
5.0%
4 14620
 
5.0%
5 14620
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 292400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 146200
50.0%
S 73100
25.0%
1 14620
 
5.0%
2 14620
 
5.0%
3 14620
 
5.0%
4 14620
 
5.0%
5 14620
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 292400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 146200
50.0%
S 73100
25.0%
1 14620
 
5.0%
2 14620
 
5.0%
3 14620
 
5.0%
4 14620
 
5.0%
5 14620
 
5.0%

Product ID
Categorical

Uniform 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size571.2 KiB
P0001
 
3655
P0002
 
3655
P0003
 
3655
P0004
 
3655
P0005
 
3655
Other values (15)
54825 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters365500
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowP0001
2nd rowP0002
3rd rowP0003
4th rowP0004
5th rowP0005

Common Values

ValueCountFrequency (%)
P0001 3655
 
5.0%
P0002 3655
 
5.0%
P0003 3655
 
5.0%
P0004 3655
 
5.0%
P0005 3655
 
5.0%
P0006 3655
 
5.0%
P0007 3655
 
5.0%
P0008 3655
 
5.0%
P0009 3655
 
5.0%
P0010 3655
 
5.0%
Other values (10) 36550
50.0%

Length

2025-03-21T13:34:23.223918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
p0001 3655
 
5.0%
p0002 3655
 
5.0%
p0003 3655
 
5.0%
p0004 3655
 
5.0%
p0005 3655
 
5.0%
p0006 3655
 
5.0%
p0007 3655
 
5.0%
p0008 3655
 
5.0%
p0009 3655
 
5.0%
p0010 3655
 
5.0%
Other values (10) 36550
50.0%

Most occurring characters

ValueCountFrequency (%)
0 186405
51.0%
P 73100
 
20.0%
1 43860
 
12.0%
2 10965
 
3.0%
3 7310
 
2.0%
4 7310
 
2.0%
5 7310
 
2.0%
6 7310
 
2.0%
7 7310
 
2.0%
8 7310
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 365500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 186405
51.0%
P 73100
 
20.0%
1 43860
 
12.0%
2 10965
 
3.0%
3 7310
 
2.0%
4 7310
 
2.0%
5 7310
 
2.0%
6 7310
 
2.0%
7 7310
 
2.0%
8 7310
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 365500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 186405
51.0%
P 73100
 
20.0%
1 43860
 
12.0%
2 10965
 
3.0%
3 7310
 
2.0%
4 7310
 
2.0%
5 7310
 
2.0%
6 7310
 
2.0%
7 7310
 
2.0%
8 7310
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 365500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 186405
51.0%
P 73100
 
20.0%
1 43860
 
12.0%
2 10965
 
3.0%
3 7310
 
2.0%
4 7310
 
2.0%
5 7310
 
2.0%
6 7310
 
2.0%
7 7310
 
2.0%
8 7310
 
2.0%

Category
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size571.2 KiB
Furniture
14699 
Toys
14643 
Clothing
14626 
Groceries
14611 
Electronics
14521 

Length

Max length11
Median length9
Mean length8.1956361
Min length4

Characters and Unicode

Total characters599101
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGroceries
2nd rowToys
3rd rowToys
4th rowToys
5th rowElectronics

Common Values

ValueCountFrequency (%)
Furniture 14699
20.1%
Toys 14643
20.0%
Clothing 14626
20.0%
Groceries 14611
20.0%
Electronics 14521
19.9%

Length

2025-03-21T13:34:23.275133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-21T13:34:23.316787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
furniture 14699
20.1%
toys 14643
20.0%
clothing 14626
20.0%
groceries 14611
20.0%
electronics 14521
19.9%

Most occurring characters

ValueCountFrequency (%)
r 73141
12.2%
i 58457
9.8%
e 58442
9.8%
o 58401
9.7%
t 43846
 
7.3%
n 43846
 
7.3%
s 43775
 
7.3%
c 43653
 
7.3%
u 29398
 
4.9%
l 29147
 
4.9%
Other values (8) 116995
19.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 599101
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 73141
12.2%
i 58457
9.8%
e 58442
9.8%
o 58401
9.7%
t 43846
 
7.3%
n 43846
 
7.3%
s 43775
 
7.3%
c 43653
 
7.3%
u 29398
 
4.9%
l 29147
 
4.9%
Other values (8) 116995
19.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 599101
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 73141
12.2%
i 58457
9.8%
e 58442
9.8%
o 58401
9.7%
t 43846
 
7.3%
n 43846
 
7.3%
s 43775
 
7.3%
c 43653
 
7.3%
u 29398
 
4.9%
l 29147
 
4.9%
Other values (8) 116995
19.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 599101
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 73141
12.2%
i 58457
9.8%
e 58442
9.8%
o 58401
9.7%
t 43846
 
7.3%
n 43846
 
7.3%
s 43775
 
7.3%
c 43653
 
7.3%
u 29398
 
4.9%
l 29147
 
4.9%
Other values (8) 116995
19.5%

Region
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size571.2 KiB
East
18349 
South
18297 
North
18228 
West
18226 

Length

Max length5
Median length4
Mean length4.499658
Min length4

Characters and Unicode

Total characters328925
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth
2nd rowSouth
3rd rowWest
4th rowNorth
5th rowEast

Common Values

ValueCountFrequency (%)
East 18349
25.1%
South 18297
25.0%
North 18228
24.9%
West 18226
24.9%

Length

2025-03-21T13:34:23.377145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-21T13:34:23.417836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
east 18349
25.1%
south 18297
25.0%
north 18228
24.9%
west 18226
24.9%

Most occurring characters

ValueCountFrequency (%)
t 73100
22.2%
s 36575
11.1%
h 36525
11.1%
o 36525
11.1%
E 18349
 
5.6%
a 18349
 
5.6%
S 18297
 
5.6%
u 18297
 
5.6%
N 18228
 
5.5%
r 18228
 
5.5%
Other values (2) 36452
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 328925
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 73100
22.2%
s 36575
11.1%
h 36525
11.1%
o 36525
11.1%
E 18349
 
5.6%
a 18349
 
5.6%
S 18297
 
5.6%
u 18297
 
5.6%
N 18228
 
5.5%
r 18228
 
5.5%
Other values (2) 36452
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 328925
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 73100
22.2%
s 36575
11.1%
h 36525
11.1%
o 36525
11.1%
E 18349
 
5.6%
a 18349
 
5.6%
S 18297
 
5.6%
u 18297
 
5.6%
N 18228
 
5.5%
r 18228
 
5.5%
Other values (2) 36452
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 328925
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 73100
22.2%
s 36575
11.1%
h 36525
11.1%
o 36525
11.1%
E 18349
 
5.6%
a 18349
 
5.6%
S 18297
 
5.6%
u 18297
 
5.6%
N 18228
 
5.5%
r 18228
 
5.5%
Other values (2) 36452
11.1%

Inventory Level
Real number (ℝ)

High correlation 

Distinct451
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean274.46988
Minimum50
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size571.2 KiB
2025-03-21T13:34:23.585803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile72
Q1162
median273
Q3387
95-th percentile478
Maximum500
Range450
Interquartile range (IQR)225

Descriptive statistics

Standard deviation129.94951
Coefficient of variation (CV)0.47345638
Kurtosis-1.2001679
Mean274.46988
Median Absolute Deviation (MAD)112
Skewness0.010115518
Sum20063748
Variance16886.876
MonotonicityNot monotonic
2025-03-21T13:34:23.648241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
168 203
 
0.3%
486 199
 
0.3%
302 195
 
0.3%
429 194
 
0.3%
201 194
 
0.3%
410 191
 
0.3%
71 191
 
0.3%
159 190
 
0.3%
288 190
 
0.3%
119 189
 
0.3%
Other values (441) 71164
97.4%
ValueCountFrequency (%)
50 130
0.2%
51 148
0.2%
52 153
0.2%
53 134
0.2%
54 165
0.2%
55 161
0.2%
56 152
0.2%
57 164
0.2%
58 173
0.2%
59 153
0.2%
ValueCountFrequency (%)
500 159
0.2%
499 164
0.2%
498 163
0.2%
497 133
0.2%
496 156
0.2%
495 171
0.2%
494 165
0.2%
493 151
0.2%
492 160
0.2%
491 141
0.2%

Units Sold
Real number (ℝ)

High correlation 

Distinct498
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136.46487
Minimum0
Maximum499
Zeros360
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size571.2 KiB
2025-03-21T13:34:23.710666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q149
median107
Q3203
95-th percentile357
Maximum499
Range499
Interquartile range (IQR)154

Descriptive statistics

Standard deviation108.91941
Coefficient of variation (CV)0.79814978
Kurtosis0.053882636
Mean136.46487
Median Absolute Deviation (MAD)69
Skewness0.90533263
Sum9975582
Variance11863.437
MonotonicityNot monotonic
2025-03-21T13:34:23.775440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 436
 
0.6%
42 418
 
0.6%
1 407
 
0.6%
33 398
 
0.5%
38 394
 
0.5%
26 394
 
0.5%
28 393
 
0.5%
31 393
 
0.5%
58 392
 
0.5%
17 390
 
0.5%
Other values (488) 69085
94.5%
ValueCountFrequency (%)
0 360
0.5%
1 407
0.6%
2 386
0.5%
3 369
0.5%
4 365
0.5%
5 359
0.5%
6 364
0.5%
7 369
0.5%
8 378
0.5%
9 331
0.5%
ValueCountFrequency (%)
499 1
 
< 0.1%
496 2
 
< 0.1%
495 1
 
< 0.1%
494 2
 
< 0.1%
493 1
 
< 0.1%
492 1
 
< 0.1%
491 3
< 0.1%
490 1
 
< 0.1%
489 2
 
< 0.1%
488 6
< 0.1%

Units Ordered
Real number (ℝ)

Distinct181
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.00447
Minimum20
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size571.2 KiB
2025-03-21T13:34:23.837598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile29
Q165
median110
Q3155
95-th percentile191
Maximum200
Range180
Interquartile range (IQR)90

Descriptive statistics

Standard deviation52.277448
Coefficient of variation (CV)0.4752302
Kurtosis-1.2023336
Mean110.00447
Median Absolute Deviation (MAD)45
Skewness0.0037865171
Sum8041327
Variance2732.9316
MonotonicityNot monotonic
2025-03-21T13:34:23.902917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 463
 
0.6%
43 462
 
0.6%
68 458
 
0.6%
71 453
 
0.6%
138 448
 
0.6%
75 446
 
0.6%
83 445
 
0.6%
105 444
 
0.6%
173 440
 
0.6%
189 437
 
0.6%
Other values (171) 68604
93.8%
ValueCountFrequency (%)
20 398
0.5%
21 413
0.6%
22 430
0.6%
23 398
0.5%
24 367
0.5%
25 399
0.5%
26 412
0.6%
27 385
0.5%
28 393
0.5%
29 401
0.5%
ValueCountFrequency (%)
200 407
0.6%
199 422
0.6%
198 397
0.5%
197 394
0.5%
196 400
0.5%
195 387
0.5%
194 399
0.5%
193 417
0.6%
192 421
0.6%
191 377
0.5%

Demand Forecast
Real number (ℝ)

High correlation 

Distinct31608
Distinct (%)43.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141.49472
Minimum-9.99
Maximum518.55
Zeros0
Zeros (%)0.0%
Negative673
Negative (%)0.9%
Memory size571.2 KiB
2025-03-21T13:34:23.968102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-9.99
5-th percentile13.85
Q153.67
median113.015
Q3208.0525
95-th percentile361.421
Maximum518.55
Range528.54
Interquartile range (IQR)154.3825

Descriptive statistics

Standard deviation109.25408
Coefficient of variation (CV)0.77214243
Kurtosis0.049345196
Mean141.49472
Median Absolute Deviation (MAD)69.705
Skewness0.89485225
Sum10343264
Variance11936.453
MonotonicityNot monotonic
2025-03-21T13:34:24.029772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46.06 12
 
< 0.1%
21.84 12
 
< 0.1%
17.33 11
 
< 0.1%
33.84 11
 
< 0.1%
43.83 11
 
< 0.1%
27.43 11
 
< 0.1%
23.35 11
 
< 0.1%
26.77 11
 
< 0.1%
38.86 11
 
< 0.1%
95.24 11
 
< 0.1%
Other values (31598) 72988
99.8%
ValueCountFrequency (%)
-9.99 1
< 0.1%
-9.97 1
< 0.1%
-9.79 1
< 0.1%
-9.67 1
< 0.1%
-9.62 1
< 0.1%
-9.58 1
< 0.1%
-9.55 1
< 0.1%
-9.47 1
< 0.1%
-9.46 1
< 0.1%
-9.29 1
< 0.1%
ValueCountFrequency (%)
518.55 1
< 0.1%
512.36 1
< 0.1%
506.37 2
< 0.1%
505.39 1
< 0.1%
504.76 1
< 0.1%
503.43 1
< 0.1%
503.12 1
< 0.1%
503.04 1
< 0.1%
502.98 1
< 0.1%
502.77 1
< 0.1%

Price
Real number (ℝ)

High correlation 

Distinct8999
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.135108
Minimum10
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size571.2 KiB
2025-03-21T13:34:24.090518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile14.33
Q132.65
median55.05
Q377.86
95-th percentile95.51
Maximum100
Range90
Interquartile range (IQR)45.21

Descriptive statistics

Standard deviation26.021945
Coefficient of variation (CV)0.47196687
Kurtosis-1.1994896
Mean55.135108
Median Absolute Deviation (MAD)22.6
Skewness-0.0027322066
Sum4030376.4
Variance677.1416
MonotonicityNot monotonic
2025-03-21T13:34:24.152284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.2 22
 
< 0.1%
82.63 20
 
< 0.1%
70.51 20
 
< 0.1%
61.29 20
 
< 0.1%
11.7 20
 
< 0.1%
93.24 19
 
< 0.1%
12.6 19
 
< 0.1%
63.98 19
 
< 0.1%
64.49 19
 
< 0.1%
42.97 19
 
< 0.1%
Other values (8989) 72903
99.7%
ValueCountFrequency (%)
10 2
 
< 0.1%
10.01 17
< 0.1%
10.02 10
< 0.1%
10.03 6
 
< 0.1%
10.04 12
< 0.1%
10.05 6
 
< 0.1%
10.06 10
< 0.1%
10.07 12
< 0.1%
10.08 7
< 0.1%
10.09 11
< 0.1%
ValueCountFrequency (%)
100 1
 
< 0.1%
99.99 5
< 0.1%
99.98 6
< 0.1%
99.97 4
< 0.1%
99.96 7
< 0.1%
99.95 9
< 0.1%
99.94 5
< 0.1%
99.93 6
< 0.1%
99.92 6
< 0.1%
99.91 5
< 0.1%

Discount
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size571.2 KiB
20
14715 
0
14662 
15
14624 
5
14591 
10
14508 

Length

Max length2
Median length2
Mean length1.5998222
Min length1

Characters and Unicode

Total characters116947
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20
2nd row20
3rd row10
4th row10
5th row0

Common Values

ValueCountFrequency (%)
20 14715
20.1%
0 14662
20.1%
15 14624
20.0%
5 14591
20.0%
10 14508
19.8%

Length

2025-03-21T13:34:24.208009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-21T13:34:24.247434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
20 14715
20.1%
0 14662
20.1%
15 14624
20.0%
5 14591
20.0%
10 14508
19.8%

Most occurring characters

ValueCountFrequency (%)
0 43885
37.5%
5 29215
25.0%
1 29132
24.9%
2 14715
 
12.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 116947
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 43885
37.5%
5 29215
25.0%
1 29132
24.9%
2 14715
 
12.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 116947
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 43885
37.5%
5 29215
25.0%
1 29132
24.9%
2 14715
 
12.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 116947
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 43885
37.5%
5 29215
25.0%
1 29132
24.9%
2 14715
 
12.6%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size571.2 KiB
Sunny
18290 
Rainy
18278 
Snowy
18272 
Cloudy
18260 

Length

Max length6
Median length5
Mean length5.2497948
Min length5

Characters and Unicode

Total characters383760
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRainy
2nd rowSunny
3rd rowSunny
4th rowCloudy
5th rowSunny

Common Values

ValueCountFrequency (%)
Sunny 18290
25.0%
Rainy 18278
25.0%
Snowy 18272
25.0%
Cloudy 18260
25.0%

Length

2025-03-21T13:34:24.300973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-21T13:34:24.339180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sunny 18290
25.0%
rainy 18278
25.0%
snowy 18272
25.0%
cloudy 18260
25.0%

Most occurring characters

ValueCountFrequency (%)
n 73130
19.1%
y 73100
19.0%
S 36562
9.5%
u 36550
9.5%
o 36532
9.5%
R 18278
 
4.8%
a 18278
 
4.8%
i 18278
 
4.8%
w 18272
 
4.8%
C 18260
 
4.8%
Other values (2) 36520
9.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 383760
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 73130
19.1%
y 73100
19.0%
S 36562
9.5%
u 36550
9.5%
o 36532
9.5%
R 18278
 
4.8%
a 18278
 
4.8%
i 18278
 
4.8%
w 18272
 
4.8%
C 18260
 
4.8%
Other values (2) 36520
9.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 383760
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 73130
19.1%
y 73100
19.0%
S 36562
9.5%
u 36550
9.5%
o 36532
9.5%
R 18278
 
4.8%
a 18278
 
4.8%
i 18278
 
4.8%
w 18272
 
4.8%
C 18260
 
4.8%
Other values (2) 36520
9.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 383760
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 73130
19.1%
y 73100
19.0%
S 36562
9.5%
u 36550
9.5%
o 36532
9.5%
R 18278
 
4.8%
a 18278
 
4.8%
i 18278
 
4.8%
w 18272
 
4.8%
C 18260
 
4.8%
Other values (2) 36520
9.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size571.2 KiB
0
36747 
1
36353 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters73100
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 36747
50.3%
1 36353
49.7%

Length

2025-03-21T13:34:24.391114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-21T13:34:24.424291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 36747
50.3%
1 36353
49.7%

Most occurring characters

ValueCountFrequency (%)
0 36747
50.3%
1 36353
49.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73100
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36747
50.3%
1 36353
49.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73100
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36747
50.3%
1 36353
49.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73100
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36747
50.3%
1 36353
49.7%

Competitor Pricing
Real number (ℝ)

High correlation 

Distinct9751
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.146077
Minimum5.03
Maximum104.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size571.2 KiB
2025-03-21T13:34:24.468938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5.03
5-th percentile14.37
Q132.68
median55.01
Q377.82
95-th percentile95.69
Maximum104.94
Range99.91
Interquartile range (IQR)45.14

Descriptive statistics

Standard deviation26.191408
Coefficient of variation (CV)0.47494598
Kurtosis-1.1696703
Mean55.146077
Median Absolute Deviation (MAD)22.55
Skewness-0.0021660999
Sum4031178.3
Variance685.98985
MonotonicityNot monotonic
2025-03-21T13:34:24.532340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 21
 
< 0.1%
46.39 21
 
< 0.1%
42.19 19
 
< 0.1%
77.99 19
 
< 0.1%
64.66 18
 
< 0.1%
94.22 18
 
< 0.1%
16.65 18
 
< 0.1%
35.28 18
 
< 0.1%
19.98 18
 
< 0.1%
74.91 18
 
< 0.1%
Other values (9741) 72912
99.7%
ValueCountFrequency (%)
5.03 1
< 0.1%
5.29 1
< 0.1%
5.31 1
< 0.1%
5.34 2
< 0.1%
5.4 1
< 0.1%
5.44 1
< 0.1%
5.45 2
< 0.1%
5.46 1
< 0.1%
5.47 1
< 0.1%
5.49 1
< 0.1%
ValueCountFrequency (%)
104.94 1
< 0.1%
104.91 1
< 0.1%
104.81 1
< 0.1%
104.74 1
< 0.1%
104.64 1
< 0.1%
104.58 1
< 0.1%
104.57 2
< 0.1%
104.56 1
< 0.1%
104.55 1
< 0.1%
104.48 1
< 0.1%

Seasonality
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size571.2 KiB
Spring
18317 
Summer
18305 
Winter
18285 
Autumn
18193 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters438600
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAutumn
2nd rowAutumn
3rd rowSummer
4th rowAutumn
5th rowSummer

Common Values

ValueCountFrequency (%)
Spring 18317
25.1%
Summer 18305
25.0%
Winter 18285
25.0%
Autumn 18193
24.9%

Length

2025-03-21T13:34:24.591702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-21T13:34:24.630854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
spring 18317
25.1%
summer 18305
25.0%
winter 18285
25.0%
autumn 18193
24.9%

Most occurring characters

ValueCountFrequency (%)
r 54907
12.5%
m 54803
12.5%
n 54795
12.5%
u 54691
12.5%
S 36622
8.3%
i 36602
8.3%
e 36590
8.3%
t 36478
8.3%
p 18317
 
4.2%
g 18317
 
4.2%
Other values (2) 36478
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 438600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 54907
12.5%
m 54803
12.5%
n 54795
12.5%
u 54691
12.5%
S 36622
8.3%
i 36602
8.3%
e 36590
8.3%
t 36478
8.3%
p 18317
 
4.2%
g 18317
 
4.2%
Other values (2) 36478
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 438600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 54907
12.5%
m 54803
12.5%
n 54795
12.5%
u 54691
12.5%
S 36622
8.3%
i 36602
8.3%
e 36590
8.3%
t 36478
8.3%
p 18317
 
4.2%
g 18317
 
4.2%
Other values (2) 36478
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 438600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 54907
12.5%
m 54803
12.5%
n 54795
12.5%
u 54691
12.5%
S 36622
8.3%
i 36602
8.3%
e 36590
8.3%
t 36478
8.3%
p 18317
 
4.2%
g 18317
 
4.2%
Other values (2) 36478
8.3%

Year
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size571.2 KiB
2022
36500 
2023
36500 
2024
 
100

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters292400
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 36500
49.9%
2023 36500
49.9%
2024 100
 
0.1%

Length

2025-03-21T13:34:24.683317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-21T13:34:24.717852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2022 36500
49.9%
2023 36500
49.9%
2024 100
 
0.1%

Most occurring characters

ValueCountFrequency (%)
2 182700
62.5%
0 73100
25.0%
3 36500
 
12.5%
4 100
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 292400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 182700
62.5%
0 73100
25.0%
3 36500
 
12.5%
4 100
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 292400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 182700
62.5%
0 73100
25.0%
3 36500
 
12.5%
4 100
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 292400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 182700
62.5%
0 73100
25.0%
3 36500
 
12.5%
4 100
 
< 0.1%

Month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5184679
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size285.7 KiB
2025-03-21T13:34:24.753366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4515644
Coefficient of variation (CV)0.52950547
Kurtosis-1.2082718
Mean6.5184679
Median Absolute Deviation (MAD)3
Skewness-0.0094523839
Sum476500
Variance11.913297
MonotonicityNot monotonic
2025-03-21T13:34:24.795815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 6300
8.6%
3 6200
8.5%
7 6200
8.5%
5 6200
8.5%
12 6200
8.5%
10 6200
8.5%
8 6200
8.5%
4 6000
8.2%
9 6000
8.2%
6 6000
8.2%
Other values (2) 11600
15.9%
ValueCountFrequency (%)
1 6300
8.6%
2 5600
7.7%
3 6200
8.5%
4 6000
8.2%
5 6200
8.5%
6 6000
8.2%
7 6200
8.5%
8 6200
8.5%
9 6000
8.2%
10 6200
8.5%
ValueCountFrequency (%)
12 6200
8.5%
11 6000
8.2%
10 6200
8.5%
9 6000
8.2%
8 6200
8.5%
7 6200
8.5%
6 6000
8.2%
5 6200
8.5%
4 6000
8.2%
3 6200
8.5%

Day
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.70041
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size285.7 KiB
2025-03-21T13:34:24.960203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8071111
Coefficient of variation (CV)0.56094783
Kurtosis-1.1937451
Mean15.70041
Median Absolute Deviation (MAD)8
Skewness0.0079550329
Sum1147700
Variance77.565206
MonotonicityNot monotonic
2025-03-21T13:34:25.011592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 2500
 
3.4%
2 2400
 
3.3%
3 2400
 
3.3%
4 2400
 
3.3%
5 2400
 
3.3%
6 2400
 
3.3%
7 2400
 
3.3%
8 2400
 
3.3%
9 2400
 
3.3%
10 2400
 
3.3%
Other values (21) 49000
67.0%
ValueCountFrequency (%)
1 2500
3.4%
2 2400
3.3%
3 2400
3.3%
4 2400
3.3%
5 2400
3.3%
6 2400
3.3%
7 2400
3.3%
8 2400
3.3%
9 2400
3.3%
10 2400
3.3%
ValueCountFrequency (%)
31 1400
1.9%
30 2200
3.0%
29 2200
3.0%
28 2400
3.3%
27 2400
3.3%
26 2400
3.3%
25 2400
3.3%
24 2400
3.3%
23 2400
3.3%
22 2400
3.3%

Interactions

2025-03-21T13:34:22.247795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:19.273764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:19.743829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:20.138971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:20.559232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:20.965933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:21.366326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:21.867571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:22.297042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:19.324192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:19.791062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:20.191247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:20.609424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:21.015823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:21.418325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:21.915152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:22.346155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:19.373005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:19.839410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:20.243665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:20.658881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:21.066180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:21.470365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:21.962345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:22.399498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:19.425363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:19.891181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:20.297078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:20.714768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:21.118907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:21.525228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:22.013956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:22.448958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:19.474447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:19.940658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:20.351088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:20.763407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:21.170530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:21.575132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:22.061770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:22.495612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:19.520487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:19.988006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:20.401789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:20.812359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:21.217165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:21.727237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:22.108280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:22.544620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:19.647197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:20.036959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:20.453666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:20.861807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:21.266910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:21.772487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:22.154346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:22.591092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:19.694711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:20.085707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:20.505082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:20.914271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:21.316086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:21.819610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-21T13:34:22.200430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-21T13:34:25.061992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
CategoryCompetitor PricingDayDemand ForecastDiscountHoliday/PromotionInventory LevelMonthPriceProduct IDRegionSeasonalityStore IDUnits OrderedUnits SoldWeather ConditionYear
Category1.0000.0000.0030.0080.0050.0000.0020.0060.0000.0040.0050.0000.0000.0000.0040.0020.000
Competitor Pricing0.0001.000-0.000-0.0000.0020.0000.009-0.0090.9940.0040.0050.0090.0000.0050.0000.0000.000
Day0.003-0.0001.0000.0060.0000.0070.0020.015-0.0000.0000.0000.0060.000-0.0010.0060.0040.066
Demand Forecast0.008-0.0000.0061.0000.0000.0000.5670.000-0.0000.0000.0000.0040.000-0.0010.9940.0070.000
Discount0.0050.0020.0000.0001.0000.0060.0000.0040.0000.0060.0000.0020.0040.0000.0000.0080.005
Holiday/Promotion0.0000.0000.0070.0000.0061.0000.0000.0130.0000.0000.0000.0000.0000.0060.0000.0000.000
Inventory Level0.0020.0090.0020.5670.0000.0001.0000.0020.0090.0000.0000.0040.0080.0010.5680.0000.006
Month0.006-0.0090.0150.0000.0040.0130.0021.000-0.0080.0000.0020.0070.000-0.0050.0000.0000.058
Price0.0000.994-0.000-0.0000.0000.0000.009-0.0081.0000.0000.0100.0050.0000.0040.0000.0000.000
Product ID0.0040.0040.0000.0000.0060.0000.0000.0000.0001.0000.0000.0040.0000.0050.0000.0040.000
Region0.0050.0050.0000.0000.0000.0000.0000.0020.0100.0001.0000.0000.0000.0000.0000.0000.003
Seasonality0.0000.0090.0060.0040.0020.0000.0040.0070.0050.0040.0001.0000.0000.0000.0030.0020.003
Store ID0.0000.0000.0000.0000.0040.0000.0080.0000.0000.0000.0000.0001.0000.0000.0000.0000.000
Units Ordered0.0000.005-0.001-0.0010.0000.0060.001-0.0050.0040.0050.0000.0000.0001.000-0.0010.0000.000
Units Sold0.0040.0000.0060.9940.0000.0000.5680.0000.0000.0000.0000.0030.000-0.0011.0000.0090.000
Weather Condition0.0020.0000.0040.0070.0080.0000.0000.0000.0000.0040.0000.0020.0000.0000.0091.0000.000
Year0.0000.0000.0660.0000.0050.0000.0060.0580.0000.0000.0030.0030.0000.0000.0000.0001.000

Missing values

2025-03-21T13:34:22.680153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-21T13:34:22.813320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DateStore IDProduct IDCategoryRegionInventory LevelUnits SoldUnits OrderedDemand ForecastPriceDiscountWeather ConditionHoliday/PromotionCompetitor PricingSeasonalityYearMonthDay
02022-01-01S001P0001GroceriesNorth23112755135.4733.5020Rainy029.69Autumn202211
12022-01-01S001P0002ToysSouth20415066144.0463.0120Sunny066.16Autumn202211
22022-01-01S001P0003ToysWest102655174.0227.9910Sunny131.32Summer202211
32022-01-01S001P0004ToysNorth4696116462.1832.7210Cloudy134.74Autumn202211
42022-01-01S001P0005ElectronicsEast166141359.2673.640Sunny068.95Summer202211
52022-01-01S001P0006GroceriesSouth138128102139.8276.8310Sunny179.35Winter202211
62022-01-01S001P0007FurnitureEast35997167108.9234.1610Rainy136.55Winter202211
72022-01-01S001P0008ClothingNorth38031254329.7397.995Cloudy0100.09Spring202211
82022-01-01S001P0009ElectronicsWest183175135174.1520.7410Cloudy017.66Autumn202211
92022-01-01S001P0010ToysSouth1082819624.4759.990Rainy161.21Winter202211
DateStore IDProduct IDCategoryRegionInventory LevelUnits SoldUnits OrderedDemand ForecastPriceDiscountWeather ConditionHoliday/PromotionCompetitor PricingSeasonalityYearMonthDay
730902024-01-01S005P0011GroceriesNorth156121156137.1286.090Snowy187.83Summer202411
730912024-01-01S005P0012ElectronicsNorth49532442317.4636.9410Sunny038.90Winter202411
730922024-01-01S005P0013ElectronicsWest38927699277.2732.050Snowy035.90Summer202411
730932024-01-01S005P0014ToysWest32819043183.9766.500Snowy165.12Winter202411
730942024-01-01S005P0015FurnitureNorth40832392336.6483.260Sunny080.96Winter202411
730952024-01-01S005P0016FurnitureEast96812718.4673.7320Snowy072.45Winter202411
730962024-01-01S005P0017ToysNorth3135110148.4382.5710Cloudy083.78Autumn202411
730972024-01-01S005P0018ClothingWest2783615139.6511.1110Rainy010.91Winter202411
730982024-01-01S005P0019ToysEast37426421270.5253.1420Rainy055.80Spring202411
730992024-01-01S005P0020GroceriesEast11761652.3378.3920Rainy179.52Spring202411